Enhancing Quality in Translation: A Scoping Review of Editing Practices and Standards

Enhancing Quality in Translation: A Scoping Review of Editing Practices and Standards

Nooni Ezdiani Yasin, Anis Shahirah Abdul Sukur^*

Translation and Interpreting Studies Section, School of Humanities, Universiti Sains Malaysia.

^*Corresponding author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.906000370

Received: 02 June 2025; Accepted: 10 June 2025; Published: 17 July 2025

ABSTRACT

This scoping review article explores the current landscape of editing practices and standards in translation, synthesising recent research to provide a comprehensive understanding of their methodologies, challenges, and impact. The study is guided by two research objectives: (1) examine the current editing and revision practices in translation, and (2) to explore how professional organisations and structured training programmes enhance editing and revision competencies among translators. Two research questions underpin the review: (1) What are the key editing and revision practices employed in translation? (2) How do professional organisations and training programmes influence the development of editing and revision skills to meet industry demands? Findings reveal a growing emphasis on systematic workflows, the integration of structured revision processes, and the influence of training programmes in fostering translator competencies. However, challenges persist, particularly in non-professional contexts and among those without formal training. In conclusion, enhancing quality in translation relies on robust editing and revision practices, supported by professional development and collaboration. This review underscores the need for ongoing research and the refinement of standards to maintain excellence in the field.

Keywords: Translation, Translation Editing, Revision, Translator, Quality Assurance

INTRODUCTION

In the ever-evolving field of translation, achieving high-quality outcomes is crucial for effective cross-cultural communication and the accurate exchange of information. At the heart of this endeavour are editing and revision practices, which play a vital role in ensuring linguistic accuracy, coherence, and stylistic appropriateness in translations. The research objectives of this study are to: (1) examine the current editing and revision practices in translation, and (2) explore the role of professional organisations, alongside structured training programs, in enhancing editing and revision competencies among translators. The research seeks to address two key questions: (1) What are the primary editing and revision practices employed in translation, and (2) how do professional organisations and training programmes influence the development of editing and revision skills in translators to meet the demands of the modern translation industry? By addressing these questions, this scoping review explores the landscape of these practices, drawing on recent scholarly research to provide a comprehensive overview of their significance, methodologies, and impact. Editing focuses on refining the target text to align with linguistic and stylistic conventions, while revision entails a comparative analysis of the source and target texts to ensure fidelity and clarity. Together, these processes form the foundation of quality assurance in translation. A study conducted by Open University Malaysia [1] regarding translation modules determine the relationship between translated module quality and translator qualifications (see Figure 1), focusing on 53 translated modules from English to Malay. Editing is required to produced quality learning materials, books, etc. As do [2] highlight, clear differentiation between editing and revision is essential for optimizing workflows and improving professional training. Revision, a complex process involving different forms, suggestions, and supervision, is not always clear. The International Organizational Standardization(ISO) standards clarify revision terms and tasks for translation service providers, based on EN 15038 [3].

Figure 1: Highest qualification of translators

Source: Nooni et al. (2019)

The importance of systematic practices is further underscored by Mossop [4], who advocates for revisions that strike a balance between clarity and fidelity to the source text. Similarly, [5] emphasise the evolution of revision practices across different translation contexts, highlighting the critical role of editor expertise in achieving consistency and quality. Effective editing ensures grammatical precision and stylistic fluency, while rigorous revision confirms the translation accurately conveys the source text’s meaning [6]. However, as [7] note, editors often face cognitive challenges, particularly when dealing with specialized texts, underscoring the need for structured workflows and standardised guidelines, such as those proposed by [8]. Professional organisations like the American Translators Association (ATA) and the Chartered Institute of Linguists (CIOL) also play pivotal roles in fostering translation quality. ATA provides resources and guidelines to uphold professionalism [9], while CIOL emphasises continuous professional development through certification and training programmes designed to equip translators with essential skills [10]. These organisations address industry challenges, including advancements in technology and the demands of multilingual projects, by fostering collaboration and promoting ethical guidelines. By doing so, they help translators navigate complex translation tasks while maintaining high quality benchmarks.

Research continues to underscore the need for effective editing and revision practices. Nitzke and Gros [6] examine editing efficiency, while [11] call for integrating revision competencies into translator education to better prepare professionals for real-world demands. However, challenges persist, particularly in non-professional contexts, as [12] highlights discrepancies in editing quality by amateurs. Studies have revealed significant disparities in revision quality between professional and non-professional practitioners, underscoring the need for specialised training and the importance of fostering collaborative relationships between editors and translators to enhance mutual understanding and improve translation outcomes [13], [14].

In summary, the effective implementation of editing and revision practices is essential for ensuring high-quality translations, as these processes enhance linguistic accuracy and coherence while addressing the challenges faced by translators. Continuous professional development and collaboration between editors and translators are vital for maintaining quality standards in the evolving landscape of translation. The novelty of this research lies in its comprehensive synthesis of editing and revision practices, its focus on the role of professional organisations and structured training programmes in developing editing competencies, and its industry-relevant insights into the alignment between academic training and real-world demands. By integrating these perspectives, this study provides a foundational analysis that bridges research, practice, and professional development in translation editing. This research uses the scoping analysis method, and the search string from the latest studies is listed in Table 1 from the Scopus database.

Table 1. Scopus Database Search String

Review Of the Study

This study is organised into three core themes to provide a comprehensive understanding of translation editing practices: (1) standards and guidelines in translation editing, (ii) practices and approaches in translation editing, and (iii) quality assurance and evaluation in translation editing. By addressing these three themes, this study aims to provide a holistic view of the translation editing landscape. It highlights the interplay between standards, practices, and quality assurance, emphasising their collective impact on the translation industry. The findings are intended to inform educators, practitioners, and policymakers, offering actionable insights to enhance the effectiveness and professionalism of translation editing. This scoping review not only maps the existing landscape but also identifies gaps and opportunities for future research, with the ultimate goal of advancing the field of translation editing.

Standards and Guidelines in Translation Editing

Standards and guidelines in translation editing theme provide the foundation for consistent, high-quality outcomes in the translation process. A study showed that questionnaire was adapted for use in patients with patellar tendinopathy. The study found good face validity, high correlation with the Blazina classification system, excellent test-retest reliability, and good internal consistency, proving the reliable and valid of the questionnaire [15]. A research enhances machine translation emotion recognition by combining word and linguistic features of environmentally friendly text with Long Short-Term Memory (LSTM) neural networks. The method addresses unbalanced emotion classification and bilingual semantic similarity degree feature, improving translation accuracy and accuracy in English environmental protection data [16]. A study on student-editors’ experiences with Wikipedia-based writing reveals gender inequality in the English version’s editorial demographic and coverage of content. The “be bold” guideline, which encourages would-be editors to go for it, has received little critical attention [17]. A research explores the ongoing issue of museum text translation in China, comparing its role and text style with Western influences, and proposing guidelines for translators to improve translation quality [18].

A study presents a preliminary architecture for a cultural tourism corpus, optimizing its structure and performance evaluation standards. The model achieves translation accuracy of above 90% and is highly sought after by 52.1% of tourists [19]. An author proposes a new approach to Machine Translation Post-Editing (MTPE) guidelines, challenging the traditional division of levels based on light and full MPTE. They argue that the role of human translators is no longer necessary in modern machine translation (MT) processes [20]. A transformer-based network architecture for instance-aware image-to-image translation, integrating global and instance-level information; learns interaction between object instances and global images, replaces layer normalization with adaptive instance normalisation (AdaIN), and introduces it for multi-domain translation [21]. Quality tourism requires quality translations to ensure satisfaction. The perceived image of a brand is formed through beliefs, attitudes, knowledge, and expectations. Translation plays a crucial role in adjusting market niches and products to existing or potential tourists [22].

MT is a technique that reduces training time and improves translation in low-resource contexts. A study conducted aims to develop baseline models for 11 Indic Languages (ILs) in a multilingual environment using Samanantar and Flores-200 corpora. The study examines the effect of related language grouping, pivot-based Multilingual neural machine translation (MNMT) models, and transliteration on ILs. Results show that using related language groups improves MNMT baselines and enhances translation quality [23]. Another study investigates factors influencing foreign users’ adoption of multilingual self-service ordering systems in fast-food restaurants. Convenience, translation quality, social anxiety, and Flow experience are identified as key factors. The study aims to improve customer evaluation criteria and establish a multilingual self-service ordering system (MSSS) model for future research [24]. A research proposes a comprehensive translation quality evaluation model combining big data analysis, machine learning, and translation theory to improve translation quality in education and research, demonstrating its effectiveness and universality [25].

ChatGPT outperforms Google Translate and DeepL Translator in poetry translation quality, retaining original poetic language and preserving rhythm and rhyme. It also comprehends common symbols, imagery, and semantic components, opening new possibilities for translating ancient literary texts [26]. A research explores the humour used in Toy Story, identifies linguistic and cultural limitations in translating humour, and assesses the quality of Modern Standard Arabic and Egyptian dialect translations. It identifies language-based and culture-based humour, highlighting their importance in translation quality [27]. A study compares revision performance of 44 institutional translators based on academic backgrounds and experience. Qualified translators with legal specialisation are most efficient, while experience partially fills training deficits. The findings support legal translator training and challenge ISO 20771:2020 qualification requirements [28]. Tourism is crucial for Spain’s economy, with 9.6 million international tourists in 2024. High-quality information in multiple languages is essential for businesses and stakeholders. A study analysed Spain’s top 20 tourist attractions’ websites and their English and French localised versions, finding poor quality [29]. A study translated the English version of the Distress Tolerance Scale into a Chinese version and validated it for measuring emotional distress tolerance in adolescents with chronic physical disease (CPD). The Chinese version was found to be reliable and valid for assessing EDT [30]. In conclusion, the reviewed studies under the standards and guidelines in translation editing theme, highlight the multifaceted nature of translation and editing, emphasising advancements in cultural adaptation, machine translation, and quality evaluation. Cross-cultural adaptations demonstrate high reliability and validity, underscoring the importance of tailoring tools to specific linguistic and cultural contexts. Machine translation innovations, including LSTM neural networks and MNMT models, address challenges like low-resource languages and semantic accuracy, showing significant potential to enhance translation quality. These advancements are complemented by studies exploring translation’s role in tourism, humour, and education, revealing gaps in localised content quality and professional training while advocating for strategies to meet user expectations and global demands effectively.

Practices and Approaches in Translation Editing

Practices and approaches in translation editing encompass a diverse range of strategies and methodologies aimed at refining translations for accuracy, coherence, and cultural relevance, ensuring they meet the intended purpose and audience expectations. Several studies highlight the application of editing strategies to ensure linguistic accuracy, cultural appropriateness, and practical relevance across various contexts. Within this theme, the paper by [31] proposes a comprehensive model to evaluate translation quality, linking legal, contextual, macro-textual, and micro textual criteria. This model reduces subjectivity and enhances predictability, highlighting the importance of legal translation expertise in enhancing professional standards. Similarly, the study by [32] proposes a Portuguese version of the ‘for the assessment of individualized risk screening criteria’ (FAIR) screening criteria for blood donation in Brazil. The Portuguese version, adapted from the FAIR criteria, aims to reduce error and increase accurate reporting of sexual behaviours. The study by [15] further aligns with this theme by demonstrating a rigorous cross-cultural adaptation and editing process of a questionnaire for use among patients with patellar tendinopathy. The translation process ensured high reliability, validity, and consistency, exemplifying effective editing practices in adapting health questionnaires for specific linguistic and cultural context.

The study adapted the Osteoporosis Knowledge Assessment Tool (OKAT) for Chinese-speaking communities in Australia, aiming to assess knowledge levels among the group. The cross-culturally adapted version improved readability and understandability, yielding higher total scores than the translated version, and demonstrating a 71.8% total response agreement [33]. In an interview on ZOOM, Gaosheng Deng and Joel Martinsen discussed the habitus and subjectivity of translators, focusing on their externalization in translation, selection of materials, strategies, and cultural differences [34].

Validated questionnaires help minimise diagnostic bias, standardise symptom assessment, and achieve comparability between studies. A European guideline recommended the use of carbohydrate perception questionnaires (aCPQ and pCPQ) for diagnosis. Clinical experts translated the questionnaires into various languages, expanding their reach [35]. A research develops an Italian translation of the Psychosocial Assessment Tool questionnaire (PAT 3.1) to assess the psychosocial risk of paediatric oncological families. The instrument will be useful for future clinical trials and clinical trials on psychosocial support interventions [36].

An exploratory pilot study explores cognitive translation and interpreting studies (CTIS) by analysing mental processes during various writing tasks, including retyping, monolingual writing, translation, revision, and monolingual text production using an infographic leaflet [37]. The study explores the perceptions of artificial intelligence (AI) language processing tools by Ukrainian Doctor of Philosophy (PhD) students, revealing mixed reactions and ethical concerns about their integration into English for Academic Purposes (EAP) courses. The results show that while students have experience with online translators, they are less familiar with writing enhancement tools. The study suggests incorporating ethical use of AI tools in EAP courses [38]. Another research examined the impact of peer-mediated Dynamic Assessment (DA) on translation revision competence among Chinese Master’s degree students. Results showed that DA improved both mediators’ and learners’ translation revision competence (TRC), but other factors also contributed. The research provides a process-oriented evaluation for translation studies [39]. A study compares revision performance of 44 institutional translators based on academic backgrounds and experience. Qualified translators with legal specialisation are most efficient, while experience partially fills training deficits. The findings support legal translator training and challenge ISO 20771:2020 qualification requirements [28].

An article explores the effectiveness of the Translation-Editing-Proofreading (TEP) model in a crowdsourcing context, focusing on factors like volunteers’ motivations and Chinese editors’ revisions. Results show no adverse effects, but further investigation is needed [40]. The paper examines a professionalising seminar for future university translators, revealing that participants felt the seminar fostered awareness of their professional domain and authentic practice, but also identified training gaps [41]. This study examines Kevin O’Rourke’s translations of traditional Korean poetry, focusing on his five-line format. It compares O’Rourke’s renditions with other translations, reveals shifts in his approach, and addresses potential issues like visual mismatch, editing errors, and viewer variability [42]. In conclusion, the reviewed studies of the practices and approaches in translation editing theme, highlight a comprehensive approach to translation editing, translation quality, emphasising legal, contextual, and textual criteria to reduce subjectivity and enhance professional standards. Cross-cultural adaptations, demonstrate the importance of tailoring tools to specific cultural contexts for reliability and usability. Advancements in machine translation, including AI-driven editing and DA, improve accuracy and competence, while ethical concerns about AI integration in education call for responsible use. Additionally, research into professional training, revision practices, and the TEP model underscores the need to align academic frameworks with practical applications, addressing gaps in expertise and fostering more effective translation practices.

Quality Assurance and Evaluation in Translation Editing

Quality assurance and evaluation are critical pillars in the field of translation editing, serving as the foundation for ensuring the accuracy, clarity, and cultural appropriateness of translated texts. A study compares neural machine translation (NMT) post-editing and human translation tasks, focusing on source text complexity and machine translation quality levels. Findings show NMT difficulty is significantly influenced by both factors, with mixed results [43]. The paper proposes an automatic scoring model for machine translation quality using deep transfer learning. This model accurately evaluates translated texts in different semantic contexts, improving accuracy and efficiency. The model also offers new ideas for future research [44]. A research conducted suggests reducing students’ anxiety and dependency on MT technology in college translation teaching by teaching them about MT, implementing flipped learning, and reevaluating teaching assessment methods [45].

A research presents the first multimodal machine translation dataset for Manipuri → English, Manipuri → Hindi, and Manipuri → German language pairs. It found that integrating multiple correlated modalities improves MT system performance in low-resource settings [46]. A study examines translation quality assessment practices of 98 faculty members in Arab universities. Results show high endorsement of both GA and TQA practices, with TQA prioritising errors, assessment methods, and objectivity. The study suggests training for improved TQA literacy [47]. The study investigates the impact of source text complexity and machine translation quality on the task difficulty of neural machine translation post-editing from English to Chinese. Results show a significant interaction between these factors, emphasizing the need for considering both factors [48]. Another research improves machine translation quality assessment accuracy and efficiency by leveraging large-scale corpora, enhancing decision-making, and reducing model bias, with future research focusing on higher-quality data and advanced assessment methods [49].

A study proposes a new approach to MTPE guidelines, challenging the traditional division of levels based on light and full MPTE. The researcher argue that the role of human translators is no longer necessary in modern MT processes [20]. A research investigates the impact of perceived post-editing self-efficacy (PESE) and MT quality on cognitive effort and post-edited quality in Chinese student translators. Results show MT quality significantly influences both processes and products [50]. The study compares translation quality in large language models using OpenAI GPT-3.5, Volctrans, and human translations. It reveals GPT-3.5 has higher quality, while Volctrans has the lowest quality, validating a new framework [51]. In conclusion, recent studies related to quality assurance and evaluation in translation editing theme, have explored various aspects of MT and its integration into translation practices, offering valuable insights into its challenges and advancements. Research has shown that source text complexity and MT quality significantly influence the difficulty of neural machine translation post-editing tasks, particularly in low-resource language settings, where multimodal approaches have demonstrated notable improvements. Additionally, new models leveraging deep transfer learning and large-scale corpora have enhanced MT quality assessment, improving accuracy and efficiency in diverse semantic contexts. These findings underline the importance of advancing translation editing, translation quality assessment practices, refining teaching methods, and reducing dependency on MT technology to foster greater student efficacy and confidence in translation education.

DISCUSSION AND CONCLUSION

The reviewed studies highlight significant advancements in translation and translation editing, spanning cultural adaptation, machine translation, and quality evaluation. Cross-cultural adaptation, highlight high reliability and validity, emphasising the need to tailor tools to specific linguistic and cultural contexts. Innovations in machine translation, including LSTM neural networks and multilingual models, address challenges in low-resource languages, semantic accuracy, and bilingual emotion recognition. These developments are complemented by research on translation’s role in tourism, humour, and education, revealing gaps in localized content quality, gender inequality in editorial practices, and professional training. The studies advocate for strategies to improve translation quality, meet user expectations, and support global communication demands.

The reviewed studies emphasise a multifaceted approach to improving translation quality, focusing on legal, contextual, and textual criteria to enhance professional standards and reduce subjectivity. Cross-cultural adaptations, such as the Portuguese FAIR criteria, questionnaire, highlight the importance of tailoring tools for cultural relevance, reliability, and usability. Advancements in machine translation and post-editing, including AI-driven tools and DA, demonstrate significant improvements in accuracy and competence. Ethical concerns regarding AI integration in education call for responsible implementation, while research on professional training, revision practices, and the Translation-Editing-Proofreading (TEP) model highlights gaps in expertise and the need to align academic frameworks with real-world applications. Collectively, these studies advocate for innovative strategies and practical approaches to enhance translation practices across diverse domains.

Recent studies highlight advancements in MT and its integration into translation practices, emphasising the impact of source text complexity and MT quality on NMT post-editing tasks. Research demonstrates that multimodal approaches improve performance in low-resource settings, while deep transfer learning models and large-scale corpora enhance MT quality assessment, boosting accuracy and efficiency. Studies also explore the influence of MT quality on cognitive effort and post-edited outputs, validating new frameworks and challenging traditional MT post-editing guidelines. Furthermore, efforts to refine teaching methods, such as incorporating flipped learning and reducing reliance on MT technology, aim to enhance student efficacy and confidence in translation education. These findings underscore the importance of advancing quality assessment practices, fostering literacy in translation tools, and addressing the evolving roles of human translators in modern MT processes.

In conclusion, the studies reviewed underscore the critical advancements in translation practices, particularly through translation editing, cultural adaptation and machine translation innovations, which collectively aim to enhance translation quality and address the complexities of diverse linguistic contexts. Emphasising the need for tailored tools and responsible integration of technology, these findings advocate for improved strategies in translation education and professional training to meet the demands of a globalised communication landscape.

Conflict Of Interest

The authors of this study disclose that they have no conflicts of interest to declare.

Funding

This research was conducted without any financial support.

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